Language: Python 

1. Introduction to AI

• ​Understand what AI is and its historical context

• Differentiate between narrow and general AI

• Explore real-world AI applications

2. Machine Learning Basics

• Learn what Machine Learning is and its types

• Discover supervised, unsupervised, and reinforcement learning

• Explore common ML algorithms: linear regression, decision trees, and k-means clustering

3. Deep Learning and Neural Networks

• Dive into neural networks and their components

• Understand activation functions and backpropagation

• Explore deep learning architectures: CNNs and RNNs

4. Natural Language Processing (NLP)

• Explore how AI understands and generates human language

• Learn about tokenisation, stemming, and sentiment analysis

• Build a basic NLP model

5. Computer Vision and Image Recognition

•  Understand AI's ability to process an analysis of visual information

• Discover image pre-processing techniques

• Build an image recognition model using Convolutional Neural Networks (CNNs)

6. AI Ethics and Future Trends

• Discuss ethical considerations in AI development and deployment

• Explore biases and fairness in AI algorithms

• Discover the future of AI, including AI in healthcare, autonomous vehicles, and more

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